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This paper describes a model for the prediction of methane and ammonia emissions from fattening pig houses. This model was validated with continuous and discrete measurements using a reference method from two manure management systems (MMS): long storage (LS) in deep pits and short storage (SS) by daily flushing of a shallow pit with sloped walls and partial manure dilution. The average calculated methane and ammonia emissions corresponded well with the measured values. Based on the calculated and measured results, the average calculated CH 4 emission (18.5 and 4.3 kg yr -1 per pig place) was in between the means from the continuous data from sensors (15.9 and 5.6 kg yr -1 per pig place) and the means from the discrete measurements using the reference method (22.0 and 3.1 kg yr -1 per pig place) for the LS and SS systems, respectively. The average calculated NH 3 emission (2.6 and 1.4 kg yr -1 per pig place) corresponded well with the continuous data (2.6 and 1.2 kg yr -1 per pig place) and the discrete measurements using the reference method (2.7 and 1.0 kg yr -1 per pig place) from LS and SS, respectively. This model was able to predict the reduction potential for methane and ammonia emissions by the application of mitigation options. Furthermore, this model can be utilized as a predictive tool, enabling timely actions to be taken based on the emission prediction. The upgraded model with robust calculation rules, extensive validations, and a simplified interface can be a useful tool to assess the current situation and the impact of mitigation measures at the farm level.

One of the main aims of European Common Agricultural Policy (CAP) has been established on a considerable reduction of greenhouse gas (GHG) emissions without any decline in productivity by 2030. This introduces some challenges in the assessment of agricultural technical efficiency. In particular, we can formulate at least two problems that are worth to be addressed: (i) possibility of treating undesirable outputs and (ii) panel data analysis of agricultural technical efficiency enriched by eco-efficiency assessment. In this study, we applied a Window Slack-Based Measurement Data Envelopment Analysis (W-SBM-DEA) model in the presence of undesirable outputs to evaluate the performance of agriculture sector of EU-27 countries during the period from 2008 to 2017. The country-level cultivated agricultural area, labor, specific costs, overheads, and depreciation were considered as the model inputs, and the gross value of country-level crop and livestock products represented the desirable outputs. The agricultural total GHG emissions at the country-level were accounted for as the undesirable output in the model. Having compared the results of different models, the substantial difference in the performance of the EU countries was registered mainly due to incorporating undesirable outputs and window analysis when using SBM-DEA model. The results of eco-efficiency assessment indicated that the Netherlands, Belgium, Italy, and Malta are the most eco-efficient countries with a score higher than 0.90, while the lowest eco-efficiency scores were reported for Slovakia, Latvia, and Estonia. The highest average eco-efficiency scores of all EU members were registered in 2011 (0.82), 2012 (0.83), and 2017 (0.84). A comparison of eco-efficiency performance between the old and new EU members indicated that the greater scores were obtained by old EU members. The variability assessment results showed a low variability and subsequently a high stability in the European agricultural sector, particularly in the Netherlands, Italy, and Malta. Based on our findings, it was concluded that for assessing agricultural technical efficiency in European agriculture in the context of eco-efficiency, application of a W-DEA model with undesirable output for a specified period of time reduces the impacts of temporary changes and provides more realistic results when comparing to models without undesirable output. These more realistic assessments of technical efficiency could help policy-makers to make more precise decisions., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Incorporating undesirable outputs in the operational assessments through the integration of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) has received great attention recently. There are many studies throughout literature that apply various methods to integrate LCA and DEA. In this case study, the six most common approaches were employed to assess the winter wheat cropping system in Poland. These six methods were: a) ignoring undesirable outputs, b) treating undesirables as inputs to the DEA model, c) data transformation, d) impact rate, e) ratio model, and f) slack based measurement DEA with undesirable outputs. The environmental impact of wheat production was assessed by determining its carbon footprint (CF). The mean CF equalled 0.45 kg CO 2eq per kg wheat grain (ranging from 0.25 to 0.67). According to the model comparison results, a slack based measurement DEA with undesirable outputs could better reflect the performance of undesirable outputs, and was selected as the most appropriate method to maximize the efficiency of winter wheat production while minimizing undesirable outputs. The advantage of applying the slack based model with undesirable outputs was that the targets presented by this model were based on existing efficient farms, as opposed to theoretical results; thus achieving these targets are feasible. The average efficiency score equalled 0.43, whereby few farms were classified as efficient farms. The results of the proposed integrated model showed a high reduction potential for mineral fertilizers (up to 595 kg ha -1 y -1 ), seed (up to 37 kg ha -1 y -1 ), and fuel (up to 75 L ha -1 y -1 ) in winter wheat farms. These results help farmers to obtain a realistic and reliable usage pattern for inputs in a winter wheat production system, whereby the greatest production can be achieved in conjunction with the lowest possible environmental impact., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Energy is one of the essential resources for human life and mainly classified as non-renewable resources. Since huge amounts of energy are consumed in the agriculture sector, an energy audit is an essential strategy in countries. Conservation agriculture as a tool for sustainable development can lead to saving agricultural resources. In the current investigation, energy audit for wheat conservation and conventional production systems was performed. For this purpose, 48 farms were selected randomly in 2016, and their energy performance was evaluated and compared. The data were analyzed to calculate energy parameters. Also, data envelopment analysis technique was used to identify the possible ways to achieve higher efficiency in farms. To this end, current and optimum situations and saving energy in different cultivation systems were determined using Charnes, Cooper, and Rhodes (CCR) model. The research results showed that the average energy ratio, net energy gain, specific energy, and energy productivity for conservation farms were 4.31, 137,656 MJ ha -1 , 5.56 MJ kg -1 , and 0.18 kg MJ -1 , respectively. Corresponded values for conventional farms were measured to be 3.03, 90,101 MJ ha -1 , 7.69 MJ kg -1 , and 0.13 kg MJ -1 , respectively. Data envelopment analysis results revealed that the highest saving energy in conventional system belongs to diesel fuel and irrigation inputs, and the least amount of energy saving was seen in human labor input. While for the conservation system, the highest and the least amount of energy saving belongs to nitrogen and human labor, respectively.

In order to achieve sustainable development in agriculture, it is necessary to quantify and compare the energy, economic, and environmental aspects of products. This paper studied the energy, economic, and greenhouse gas (GHG) emission patterns in broiler chicken farms in the Alborz province of Iran. We studied the effect of the broiler farm size as different production systems on the energy, economic, and environmental indices. Energy use efficiency (EUE) and benefit-cost ratio (BCR) were 0.16 and 1.11, respectively. Diesel fuel and feed contributed the most in total energy inputs, while feed and chicks were the most important inputs in economic analysis. GHG emission calculations showed that production of 1000 birds produces 19.13 t CO 2-eq and feed had the highest share in total GHG emission. Total GHG emissions based on different functional units were 8.5 t CO 2-eq per t of carcass and 6.83 kg CO 2-eq per kg live weight. Results of farm size effect on EUE revealed that large farms had better energy management. For BCR, there was no significant difference between farms. Lower total GHG emissions were reported for large farms, caused by better management of inputs and fewer bird losses. Large farms with more investment had more efficient equipment, resulting in a decrease of the input consumption. In view of our study, it is recommended to support the small-scale broiler industry by providing subsidies to promote the use of high-efficiency equipment. To decrease the amount of energy usage and GHG emissions, replacing heaters (which use diesel fuel) with natural gas heaters can be considered. In addition to the above recommendations, the use of energy saving light bulbs may reduce broiler farm electricity consumption.